Overview

Dataset statistics

Number of variables24
Number of observations6743404
Missing cells0
Missing cells (%)0.0%
Duplicate rows31
Duplicate rows (%)< 0.1%
Total size in memory6.0 GiB
Average record size in memory955.0 B

Variable types

DateTime1
Numeric10
Categorical8
Text5

Alerts

Dataset has 31 (< 0.1%) duplicate rowsDuplicates
distance_type is highly imbalanced (64.0%)Imbalance
delay_carrier is highly skewed (γ1 = 22.75356143)Skewed
delay_weather is highly skewed (γ1 = 45.85829053)Skewed
delay_nas is highly skewed (γ1 = 22.64631668)Skewed
delay_security is highly skewed (γ1 = 288.4643263)Skewed
dep_delay has 314587 (4.7%) zerosZeros
arr_delay has 124753 (1.9%) zerosZeros
delay_carrier has 5955514 (88.3%) zerosZeros
delay_weather has 6671405 (98.9%) zerosZeros
delay_nas has 6082619 (90.2%) zerosZeros
delay_security has 6735256 (99.9%) zerosZeros
delay_lastaircraft has 6032286 (89.5%) zerosZeros

Reproduction

Analysis started2024-06-17 11:51:53.295062
Analysis finished2024-06-17 11:53:57.198842
Duration2 minutes and 3.9 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Distinct365
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.4 MiB
Minimum2023-01-01 00:00:00
Maximum2023-12-31 00:00:00
2024-06-17T14:53:57.246833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:57.315804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

day_of_week
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9827933
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 MiB
2024-06-17T14:53:57.452289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0017618
Coefficient of variation (CV)0.50260249
Kurtosis-1.2323597
Mean3.9827933
Median Absolute Deviation (MAD)2
Skewness0.011062589
Sum26857584
Variance4.0070504
MonotonicityNot monotonic
2024-06-17T14:53:57.509555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 1003622
14.9%
4 998363
14.8%
1 996633
14.8%
7 984934
14.6%
3 951327
14.1%
2 937570
13.9%
6 870955
12.9%
ValueCountFrequency (%)
1 996633
14.8%
2 937570
13.9%
3 951327
14.1%
4 998363
14.8%
5 1003622
14.9%
6 870955
12.9%
7 984934
14.6%
ValueCountFrequency (%)
7 984934
14.6%
6 870955
12.9%
5 1003622
14.9%
4 998363
14.8%
3 951327
14.1%
2 937570
13.9%
1 996633
14.8%

airline
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size495.2 MiB
Southwest Airlines Co.
1421238 
Delta Air Lines Inc
972931 
American Airlines Inc.
928058 
United Air Lines Inc.
720032 
Skywest Airlines Inc.
664861 
Other values (10)
2036284 

Length

Max length28
Median length22
Mean length19.998374
Min length12

Characters and Unicode

Total characters134857118
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEndeavor Air
2nd rowEndeavor Air
3rd rowEndeavor Air
4th rowEndeavor Air
5th rowEndeavor Air

Common Values

ValueCountFrequency (%)
Southwest Airlines Co. 1421238
21.1%
Delta Air Lines Inc 972931
14.4%
American Airlines Inc. 928058
13.8%
United Air Lines Inc. 720032
10.7%
Skywest Airlines Inc. 664861
9.9%
Republic Airways 286490
 
4.2%
JetBlue Airways 267915
 
4.0%
Spirit Air Lines 258838
 
3.8%
Alaska Airlines Inc. 242643
 
3.6%
American Eagle Airlines Inc. 224695
 
3.3%
Other values (5) 755703
11.2%

Length

2024-06-17T14:53:57.577352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 4006521
19.0%
airlines 3925868
18.6%
air 2263131
10.7%
lines 1951801
9.3%
southwest 1421238
 
6.7%
co 1421238
 
6.7%
american 1152753
 
5.5%
delta 972931
 
4.6%
united 720032
 
3.4%
skywest 664861
 
3.2%
Other values (11) 2590689
12.3%

Most occurring characters

ValueCountFrequency (%)
i 15745592
11.7%
14347659
 
10.6%
e 12341288
 
9.2%
n 12321606
 
9.1%
s 8760816
 
6.5%
r 8698818
 
6.5%
A 8444297
 
6.3%
l 6149392
 
4.6%
t 6014937
 
4.5%
c 5445764
 
4.0%
Other values (27) 36586949
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134857118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 15745592
11.7%
14347659
 
10.6%
e 12341288
 
9.2%
n 12321606
 
9.1%
s 8760816
 
6.5%
r 8698818
 
6.5%
A 8444297
 
6.3%
l 6149392
 
4.6%
t 6014937
 
4.5%
c 5445764
 
4.0%
Other values (27) 36586949
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134857118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 15745592
11.7%
14347659
 
10.6%
e 12341288
 
9.2%
n 12321606
 
9.1%
s 8760816
 
6.5%
r 8698818
 
6.5%
A 8444297
 
6.3%
l 6149392
 
4.6%
t 6014937
 
4.5%
c 5445764
 
4.0%
Other values (27) 36586949
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134857118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 15745592
11.7%
14347659
 
10.6%
e 12341288
 
9.2%
n 12321606
 
9.1%
s 8760816
 
6.5%
r 8698818
 
6.5%
A 8444297
 
6.3%
l 6149392
 
4.6%
t 6014937
 
4.5%
c 5445764
 
4.0%
Other values (27) 36586949
27.1%
Distinct5963
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size405.1 MiB
2024-06-17T14:53:57.728735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.997429
Min length2

Characters and Unicode

Total characters40443087
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowN605LR
2nd rowN605LR
3rd rowN331PQ
4th rowN906XJ
5th rowN337PQ
ValueCountFrequency (%)
n488ha 3327
 
< 0.1%
n487ha 3315
 
< 0.1%
n486ha 3306
 
< 0.1%
n483ha 3222
 
< 0.1%
n484ha 3221
 
< 0.1%
n485ha 3199
 
< 0.1%
n479ha 3190
 
< 0.1%
n475ha 3160
 
< 0.1%
n495ha 3150
 
< 0.1%
n480ha 3107
 
< 0.1%
Other values (5953) 6711207
99.5%
2024-06-17T14:53:57.967714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 9256873
22.9%
8 2779496
 
6.9%
7 2486728
 
6.1%
2 2477915
 
6.1%
3 2475189
 
6.1%
5 2146884
 
5.3%
1 2140191
 
5.3%
9 2098277
 
5.2%
6 2056955
 
5.1%
4 2047117
 
5.1%
Other values (25) 10477462
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40443087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 9256873
22.9%
8 2779496
 
6.9%
7 2486728
 
6.1%
2 2477915
 
6.1%
3 2475189
 
6.1%
5 2146884
 
5.3%
1 2140191
 
5.3%
9 2098277
 
5.2%
6 2056955
 
5.1%
4 2047117
 
5.1%
Other values (25) 10477462
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40443087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 9256873
22.9%
8 2779496
 
6.9%
7 2486728
 
6.1%
2 2477915
 
6.1%
3 2475189
 
6.1%
5 2146884
 
5.3%
1 2140191
 
5.3%
9 2098277
 
5.2%
6 2056955
 
5.1%
4 2047117
 
5.1%
Other values (25) 10477462
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40443087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 9256873
22.9%
8 2779496
 
6.9%
7 2486728
 
6.1%
2 2477915
 
6.1%
3 2475189
 
6.1%
5 2146884
 
5.3%
1 2140191
 
5.3%
9 2098277
 
5.2%
6 2056955
 
5.1%
4 2047117
 
5.1%
Other values (25) 10477462
25.9%
Distinct350
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size385.9 MiB
2024-06-17T14:53:58.167721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters20230212
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBDL
2nd rowBDL
3rd rowBDL
4th rowBDL
5th rowBDL
ValueCountFrequency (%)
atl 332935
 
4.9%
den 284200
 
4.2%
dfw 280021
 
4.2%
ord 255071
 
3.8%
clt 192870
 
2.9%
lax 192260
 
2.9%
las 188206
 
2.8%
phx 175144
 
2.6%
sea 162441
 
2.4%
mco 161846
 
2.4%
Other values (340) 4518410
67.0%
2024-06-17T14:53:58.422417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2310561
 
11.4%
L 1868924
 
9.2%
S 1731888
 
8.6%
D 1586090
 
7.8%
T 1071694
 
5.3%
O 1033706
 
5.1%
C 1021112
 
5.0%
M 905267
 
4.5%
F 835550
 
4.1%
W 789662
 
3.9%
Other values (16) 7075758
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20230212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2310561
 
11.4%
L 1868924
 
9.2%
S 1731888
 
8.6%
D 1586090
 
7.8%
T 1071694
 
5.3%
O 1033706
 
5.1%
C 1021112
 
5.0%
M 905267
 
4.5%
F 835550
 
4.1%
W 789662
 
3.9%
Other values (16) 7075758
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20230212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2310561
 
11.4%
L 1868924
 
9.2%
S 1731888
 
8.6%
D 1586090
 
7.8%
T 1071694
 
5.3%
O 1033706
 
5.1%
C 1021112
 
5.0%
M 905267
 
4.5%
F 835550
 
4.1%
W 789662
 
3.9%
Other values (16) 7075758
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20230212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2310561
 
11.4%
L 1868924
 
9.2%
S 1731888
 
8.6%
D 1586090
 
7.8%
T 1071694
 
5.3%
O 1033706
 
5.1%
C 1021112
 
5.0%
M 905267
 
4.5%
F 835550
 
4.1%
W 789662
 
3.9%
Other values (16) 7075758
35.0%
Distinct344
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size450.5 MiB
2024-06-17T14:53:58.584166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.045103
Min length8

Characters and Unicode

Total characters87968400
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHartford, CT
2nd rowHartford, CT
3rd rowHartford, CT
4th rowHartford, CT
5th rowHartford, CT
ValueCountFrequency (%)
ca 729627
 
4.6%
tx 707555
 
4.5%
fl 600100
 
3.8%
ny 367049
 
2.3%
ga 357008
 
2.3%
san 352217
 
2.2%
il 351390
 
2.2%
chicago 338766
 
2.2%
new 337488
 
2.1%
atlanta 332935
 
2.1%
Other values (418) 11242964
71.5%
2024-06-17T14:53:58.846247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8973695
 
10.2%
, 6743404
 
7.7%
a 6726644
 
7.6%
o 4843270
 
5.5%
e 4645987
 
5.3%
n 4315860
 
4.9%
t 4204810
 
4.8%
l 3879759
 
4.4%
i 3337094
 
3.8%
r 3178163
 
3.6%
Other values (47) 37119714
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87968400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8973695
 
10.2%
, 6743404
 
7.7%
a 6726644
 
7.6%
o 4843270
 
5.5%
e 4645987
 
5.3%
n 4315860
 
4.9%
t 4204810
 
4.8%
l 3879759
 
4.4%
i 3337094
 
3.8%
r 3178163
 
3.6%
Other values (47) 37119714
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87968400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8973695
 
10.2%
, 6743404
 
7.7%
a 6726644
 
7.6%
o 4843270
 
5.5%
e 4645987
 
5.3%
n 4315860
 
4.9%
t 4204810
 
4.8%
l 3879759
 
4.4%
i 3337094
 
3.8%
r 3178163
 
3.6%
Other values (47) 37119714
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87968400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8973695
 
10.2%
, 6743404
 
7.7%
a 6726644
 
7.6%
o 4843270
 
5.5%
e 4645987
 
5.3%
n 4315860
 
4.9%
t 4204810
 
4.8%
l 3879759
 
4.4%
i 3337094
 
3.8%
r 3178163
 
3.6%
Other values (47) 37119714
42.2%

deptime_label
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size415.7 MiB
Morning
2611567 
Afternoon
2363360 
Evening
1557318 
Night
 
211159

Length

Max length9
Median length7
Mean length7.6383129
Min length5

Characters and Unicode

Total characters51508230
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMorning
2nd rowMorning
3rd rowMorning
4th rowMorning
5th rowMorning

Common Values

ValueCountFrequency (%)
Morning 2611567
38.7%
Afternoon 2363360
35.0%
Evening 1557318
23.1%
Night 211159
 
3.1%

Length

2024-06-17T14:53:58.923414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T14:53:58.980362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
morning 2611567
38.7%
afternoon 2363360
35.0%
evening 1557318
23.1%
night 211159
 
3.1%

Most occurring characters

ValueCountFrequency (%)
n 13064490
25.4%
o 7338287
14.2%
r 4974927
 
9.7%
i 4380044
 
8.5%
g 4380044
 
8.5%
e 3920678
 
7.6%
M 2611567
 
5.1%
t 2574519
 
5.0%
A 2363360
 
4.6%
f 2363360
 
4.6%
Other values (4) 3536954
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51508230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 13064490
25.4%
o 7338287
14.2%
r 4974927
 
9.7%
i 4380044
 
8.5%
g 4380044
 
8.5%
e 3920678
 
7.6%
M 2611567
 
5.1%
t 2574519
 
5.0%
A 2363360
 
4.6%
f 2363360
 
4.6%
Other values (4) 3536954
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51508230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 13064490
25.4%
o 7338287
14.2%
r 4974927
 
9.7%
i 4380044
 
8.5%
g 4380044
 
8.5%
e 3920678
 
7.6%
M 2611567
 
5.1%
t 2574519
 
5.0%
A 2363360
 
4.6%
f 2363360
 
4.6%
Other values (4) 3536954
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51508230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 13064490
25.4%
o 7338287
14.2%
r 4974927
 
9.7%
i 4380044
 
8.5%
g 4380044
 
8.5%
e 3920678
 
7.6%
M 2611567
 
5.1%
t 2574519
 
5.0%
A 2363360
 
4.6%
f 2363360
 
4.6%
Other values (4) 3536954
 
6.9%

dep_delay
Real number (ℝ)

ZEROS 

Distinct1854
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.200987
Minimum-99
Maximum4413
Zeros314587
Zeros (%)4.7%
Negative3873058
Negative (%)57.4%
Memory size51.4 MiB
2024-06-17T14:53:59.046113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-10
Q1-5
median-2
Q39
95-th percentile78
Maximum4413
Range4512
Interquartile range (IQR)14

Descriptive statistics

Standard deviation55.079361
Coefficient of variation (CV)4.5143367
Kurtosis268.2125
Mean12.200987
Median Absolute Deviation (MAD)5
Skewness12.011317
Sum82276182
Variance3033.736
MonotonicityNot monotonic
2024-06-17T14:53:59.114767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 504339
 
7.5%
-4 470790
 
7.0%
-3 455316
 
6.8%
-2 413304
 
6.1%
-6 402407
 
6.0%
-1 369791
 
5.5%
-7 340582
 
5.1%
0 314587
 
4.7%
-8 272735
 
4.0%
-9 202664
 
3.0%
Other values (1844) 2996889
44.4%
ValueCountFrequency (%)
-99 1
 
< 0.1%
-72 1
 
< 0.1%
-68 1
 
< 0.1%
-59 3
< 0.1%
-55 1
 
< 0.1%
-53 1
 
< 0.1%
-52 2
< 0.1%
-51 1
 
< 0.1%
-50 1
 
< 0.1%
-49 1
 
< 0.1%
ValueCountFrequency (%)
4413 1
< 0.1%
3786 1
< 0.1%
3695 1
< 0.1%
3518 1
< 0.1%
3445 1
< 0.1%
3343 1
< 0.1%
3249 1
< 0.1%
3238 1
< 0.1%
3221 1
< 0.1%
3024 1
< 0.1%

dep_delay_tag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size373.0 MiB
0
4187645 
1
2555759 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6743404
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4187645
62.1%
1 2555759
37.9%

Length

2024-06-17T14:53:59.179916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T14:53:59.224646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 4187645
62.1%
1 2555759
37.9%

Most occurring characters

ValueCountFrequency (%)
0 4187645
62.1%
1 2555759
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6743404
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4187645
62.1%
1 2555759
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6743404
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4187645
62.1%
1 2555759
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6743404
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4187645
62.1%
1 2555759
37.9%

dep_delay_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size429.1 MiB
Low <5min
5409737 
Medium >15min
877998 
Hight >60min
 
455669

Length

Max length13
Median length9
Mean length9.7235217
Min length9

Characters and Unicode

Total characters65569635
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow <5min
2nd rowLow <5min
3rd rowLow <5min
4th rowLow <5min
5th rowLow <5min

Common Values

ValueCountFrequency (%)
Low <5min 5409737
80.2%
Medium >15min 877998
 
13.0%
Hight >60min 455669
 
6.8%

Length

2024-06-17T14:53:59.290175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T14:53:59.348104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
low 5409737
40.1%
5min 5409737
40.1%
medium 877998
 
6.5%
15min 877998
 
6.5%
hight 455669
 
3.4%
60min 455669
 
3.4%

Most occurring characters

ValueCountFrequency (%)
i 8077071
12.3%
m 7621402
11.6%
6743404
10.3%
n 6743404
10.3%
5 6287735
9.6%
L 5409737
8.3%
w 5409737
8.3%
< 5409737
8.3%
o 5409737
8.3%
> 1333667
 
2.0%
Other values (11) 7124004
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65569635
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 8077071
12.3%
m 7621402
11.6%
6743404
10.3%
n 6743404
10.3%
5 6287735
9.6%
L 5409737
8.3%
w 5409737
8.3%
< 5409737
8.3%
o 5409737
8.3%
> 1333667
 
2.0%
Other values (11) 7124004
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65569635
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 8077071
12.3%
m 7621402
11.6%
6743404
10.3%
n 6743404
10.3%
5 6287735
9.6%
L 5409737
8.3%
w 5409737
8.3%
< 5409737
8.3%
o 5409737
8.3%
> 1333667
 
2.0%
Other values (11) 7124004
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65569635
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 8077071
12.3%
m 7621402
11.6%
6743404
10.3%
n 6743404
10.3%
5 6287735
9.6%
L 5409737
8.3%
w 5409737
8.3%
< 5409737
8.3%
o 5409737
8.3%
> 1333667
 
2.0%
Other values (11) 7124004
10.9%
Distinct350
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size385.9 MiB
2024-06-17T14:53:59.531643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters20230212
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLGA
2nd rowLGA
3rd rowLGA
4th rowLGA
5th rowLGA
ValueCountFrequency (%)
atl 332941
 
4.9%
den 283563
 
4.2%
dfw 279733
 
4.1%
ord 254775
 
3.8%
clt 192911
 
2.9%
lax 192417
 
2.9%
las 188243
 
2.8%
phx 175196
 
2.6%
sea 162323
 
2.4%
mco 161373
 
2.4%
Other values (340) 4519929
67.0%
2024-06-17T14:53:59.785849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2310587
 
11.4%
L 1868994
 
9.2%
S 1733227
 
8.6%
D 1584874
 
7.8%
T 1072291
 
5.3%
O 1032990
 
5.1%
C 1021074
 
5.0%
M 904967
 
4.5%
F 835174
 
4.1%
W 789110
 
3.9%
Other values (16) 7076924
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20230212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2310587
 
11.4%
L 1868994
 
9.2%
S 1733227
 
8.6%
D 1584874
 
7.8%
T 1072291
 
5.3%
O 1032990
 
5.1%
C 1021074
 
5.0%
M 904967
 
4.5%
F 835174
 
4.1%
W 789110
 
3.9%
Other values (16) 7076924
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20230212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2310587
 
11.4%
L 1868994
 
9.2%
S 1733227
 
8.6%
D 1584874
 
7.8%
T 1072291
 
5.3%
O 1032990
 
5.1%
C 1021074
 
5.0%
M 904967
 
4.5%
F 835174
 
4.1%
W 789110
 
3.9%
Other values (16) 7076924
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20230212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2310587
 
11.4%
L 1868994
 
9.2%
S 1733227
 
8.6%
D 1584874
 
7.8%
T 1072291
 
5.3%
O 1032990
 
5.1%
C 1021074
 
5.0%
M 904967
 
4.5%
F 835174
 
4.1%
W 789110
 
3.9%
Other values (16) 7076924
35.0%
Distinct344
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size450.5 MiB
2024-06-17T14:53:59.941432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.045968
Min length8

Characters and Unicode

Total characters87974235
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York, NY
2nd rowNew York, NY
3rd rowNew York, NY
4th rowNew York, NY
5th rowNew York, NY
ValueCountFrequency (%)
ca 730230
 
4.6%
tx 707058
 
4.5%
fl 599542
 
3.8%
ny 366899
 
2.3%
ga 357014
 
2.3%
san 352645
 
2.2%
il 350948
 
2.2%
chicago 338319
 
2.2%
new 337294
 
2.1%
atlanta 332941
 
2.1%
Other values (418) 11245209
71.5%
2024-06-17T14:54:00.163541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8974695
 
10.2%
, 6743404
 
7.7%
a 6727616
 
7.6%
o 4842443
 
5.5%
e 4646574
 
5.3%
n 4316955
 
4.9%
t 4205119
 
4.8%
l 3880082
 
4.4%
i 3338106
 
3.8%
r 3176719
 
3.6%
Other values (47) 37122522
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87974235
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8974695
 
10.2%
, 6743404
 
7.7%
a 6727616
 
7.6%
o 4842443
 
5.5%
e 4646574
 
5.3%
n 4316955
 
4.9%
t 4205119
 
4.8%
l 3880082
 
4.4%
i 3338106
 
3.8%
r 3176719
 
3.6%
Other values (47) 37122522
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87974235
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8974695
 
10.2%
, 6743404
 
7.7%
a 6727616
 
7.6%
o 4842443
 
5.5%
e 4646574
 
5.3%
n 4316955
 
4.9%
t 4205119
 
4.8%
l 3880082
 
4.4%
i 3338106
 
3.8%
r 3176719
 
3.6%
Other values (47) 37122522
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87974235
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8974695
 
10.2%
, 6743404
 
7.7%
a 6727616
 
7.6%
o 4842443
 
5.5%
e 4646574
 
5.3%
n 4316955
 
4.9%
t 4205119
 
4.8%
l 3880082
 
4.4%
i 3338106
 
3.8%
r 3176719
 
3.6%
Other values (47) 37122522
42.2%

arr_delay
Real number (ℝ)

ZEROS 

Distinct1880
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6271515
Minimum-119
Maximum4405
Zeros124753
Zeros (%)1.9%
Negative4146121
Negative (%)61.5%
Memory size51.4 MiB
2024-06-17T14:54:00.248541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-119
5-th percentile-27
Q1-15
median-6
Q39
95-th percentile78
Maximum4405
Range4524
Interquartile range (IQR)24

Descriptive statistics

Standard deviation57.078921
Coefficient of variation (CV)8.6128892
Kurtosis235.04159
Mean6.6271515
Median Absolute Deviation (MAD)11
Skewness10.934348
Sum44689560
Variance3258.0033
MonotonicityNot monotonic
2024-06-17T14:54:00.318625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11 189215
 
2.8%
-10 189118
 
2.8%
-12 188072
 
2.8%
-9 185850
 
2.8%
-13 184325
 
2.7%
-8 182425
 
2.7%
-14 179315
 
2.7%
-7 176693
 
2.6%
-15 172031
 
2.6%
-6 170461
 
2.5%
Other values (1870) 4925899
73.0%
ValueCountFrequency (%)
-119 1
 
< 0.1%
-98 1
 
< 0.1%
-97 1
 
< 0.1%
-96 1
 
< 0.1%
-94 1
 
< 0.1%
-92 2
 
< 0.1%
-91 1
 
< 0.1%
-89 1
 
< 0.1%
-88 1
 
< 0.1%
-86 5
< 0.1%
ValueCountFrequency (%)
4405 1
< 0.1%
3795 1
< 0.1%
3680 1
< 0.1%
3502 1
< 0.1%
3424 1
< 0.1%
3337 1
< 0.1%
3246 1
< 0.1%
3241 1
< 0.1%
3237 1
< 0.1%
3063 1
< 0.1%

arr_delay_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size429.1 MiB
Low <5min
5403727 
Medium >15min
885990 
Hight >60min
 
453687

Length

Max length13
Median length9
Mean length9.7273806
Min length9

Characters and Unicode

Total characters65595657
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow <5min
2nd rowLow <5min
3rd rowLow <5min
4th rowLow <5min
5th rowLow <5min

Common Values

ValueCountFrequency (%)
Low <5min 5403727
80.1%
Medium >15min 885990
 
13.1%
Hight >60min 453687
 
6.7%

Length

2024-06-17T14:54:00.391513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T14:54:00.447741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
low 5403727
40.1%
5min 5403727
40.1%
medium 885990
 
6.6%
15min 885990
 
6.6%
hight 453687
 
3.4%
60min 453687
 
3.4%

Most occurring characters

ValueCountFrequency (%)
i 8083081
12.3%
m 7629394
11.6%
6743404
10.3%
n 6743404
10.3%
5 6289717
9.6%
L 5403727
8.2%
w 5403727
8.2%
< 5403727
8.2%
o 5403727
8.2%
> 1339677
 
2.0%
Other values (11) 7152072
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65595657
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 8083081
12.3%
m 7629394
11.6%
6743404
10.3%
n 6743404
10.3%
5 6289717
9.6%
L 5403727
8.2%
w 5403727
8.2%
< 5403727
8.2%
o 5403727
8.2%
> 1339677
 
2.0%
Other values (11) 7152072
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65595657
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 8083081
12.3%
m 7629394
11.6%
6743404
10.3%
n 6743404
10.3%
5 6289717
9.6%
L 5403727
8.2%
w 5403727
8.2%
< 5403727
8.2%
o 5403727
8.2%
> 1339677
 
2.0%
Other values (11) 7152072
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65595657
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 8083081
12.3%
m 7629394
11.6%
6743404
10.3%
n 6743404
10.3%
5 6289717
9.6%
L 5403727
8.2%
w 5403727
8.2%
< 5403727
8.2%
o 5403727
8.2%
> 1339677
 
2.0%
Other values (11) 7152072
10.9%

flight_duration
Real number (ℝ)

Distinct724
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.29778
Minimum0
Maximum795
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size51.4 MiB
2024-06-17T14:54:00.508623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile58
Q187
median124
Q3171
95-th percentile298
Maximum795
Range795
Interquartile range (IQR)84

Descriptive statistics

Standard deviation72.872157
Coefficient of variation (CV)0.51941063
Kurtosis2.4147395
Mean140.29778
Median Absolute Deviation (MAD)41
Skewness1.3861561
Sum9.460846 × 108
Variance5310.3513
MonotonicityNot monotonic
2024-06-17T14:54:00.590138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 53005
 
0.8%
82 52586
 
0.8%
79 52293
 
0.8%
80 52180
 
0.8%
83 51944
 
0.8%
78 51746
 
0.8%
84 51544
 
0.8%
77 51196
 
0.8%
85 51030
 
0.8%
76 50814
 
0.8%
Other values (714) 6225066
92.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
15 3
 
< 0.1%
16 9
 
< 0.1%
17 28
 
< 0.1%
18 36
< 0.1%
19 45
< 0.1%
20 49
< 0.1%
21 65
< 0.1%
22 81
< 0.1%
23 73
< 0.1%
ValueCountFrequency (%)
795 1
< 0.1%
759 1
< 0.1%
749 1
< 0.1%
744 1
< 0.1%
742 2
< 0.1%
736 1
< 0.1%
735 2
< 0.1%
734 1
< 0.1%
732 1
< 0.1%
731 1
< 0.1%

distance_type
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size483.1 MiB
Short Haul >1500Mi
5872159 
Medium Haul <3000Mi
857184 
Long Haul <6000Mi
 
14061

Length

Max length19
Median length18
Mean length18.125029
Min length17

Characters and Unicode

Total characters122224395
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShort Haul >1500Mi
2nd rowShort Haul >1500Mi
3rd rowShort Haul >1500Mi
4th rowShort Haul >1500Mi
5th rowShort Haul >1500Mi

Common Values

ValueCountFrequency (%)
Short Haul >1500Mi 5872159
87.1%
Medium Haul <3000Mi 857184
 
12.7%
Long Haul <6000Mi 14061
 
0.2%

Length

2024-06-17T14:54:00.739590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T14:54:00.834129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
haul 6743404
33.3%
short 5872159
29.0%
1500mi 5872159
29.0%
medium 857184
 
4.2%
3000mi 857184
 
4.2%
long 14061
 
0.1%
6000mi 14061
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 14358053
 
11.7%
13486808
 
11.0%
i 7600588
 
6.2%
M 7600588
 
6.2%
u 7600588
 
6.2%
H 6743404
 
5.5%
a 6743404
 
5.5%
l 6743404
 
5.5%
o 5886220
 
4.8%
S 5872159
 
4.8%
Other values (15) 39589179
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122224395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14358053
 
11.7%
13486808
 
11.0%
i 7600588
 
6.2%
M 7600588
 
6.2%
u 7600588
 
6.2%
H 6743404
 
5.5%
a 6743404
 
5.5%
l 6743404
 
5.5%
o 5886220
 
4.8%
S 5872159
 
4.8%
Other values (15) 39589179
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122224395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14358053
 
11.7%
13486808
 
11.0%
i 7600588
 
6.2%
M 7600588
 
6.2%
u 7600588
 
6.2%
H 6743404
 
5.5%
a 6743404
 
5.5%
l 6743404
 
5.5%
o 5886220
 
4.8%
S 5872159
 
4.8%
Other values (15) 39589179
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122224395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14358053
 
11.7%
13486808
 
11.0%
i 7600588
 
6.2%
M 7600588
 
6.2%
u 7600588
 
6.2%
H 6743404
 
5.5%
a 6743404
 
5.5%
l 6743404
 
5.5%
o 5886220
 
4.8%
S 5872159
 
4.8%
Other values (15) 39589179
32.4%

delay_carrier
Real number (ℝ)

SKEWED  ZEROS 

Distinct1650
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1698036
Minimum0
Maximum3957
Zeros5955514
Zeros (%)88.3%
Negative0
Negative (%)0.0%
Memory size51.4 MiB
2024-06-17T14:54:00.913086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile23
Maximum3957
Range3957
Interquartile range (IQR)0

Descriptive statistics

Standard deviation36.457324
Coefficient of variation (CV)7.0519746
Kurtosis861.84351
Mean5.1698036
Median Absolute Deviation (MAD)0
Skewness22.753561
Sum34862074
Variance1329.1364
MonotonicityNot monotonic
2024-06-17T14:54:00.993128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5955514
88.3%
1 25940
 
0.4%
2 25697
 
0.4%
3 24727
 
0.4%
6 24624
 
0.4%
4 23830
 
0.4%
15 23740
 
0.4%
7 22964
 
0.3%
5 22733
 
0.3%
8 21392
 
0.3%
Other values (1640) 572243
 
8.5%
ValueCountFrequency (%)
0 5955514
88.3%
1 25940
 
0.4%
2 25697
 
0.4%
3 24727
 
0.4%
4 23830
 
0.4%
5 22733
 
0.3%
6 24624
 
0.4%
7 22964
 
0.3%
8 21392
 
0.3%
9 20398
 
0.3%
ValueCountFrequency (%)
3957 1
< 0.1%
3786 1
< 0.1%
3502 1
< 0.1%
3424 1
< 0.1%
3337 1
< 0.1%
3246 1
< 0.1%
3221 1
< 0.1%
3045 1
< 0.1%
3024 1
< 0.1%
2998 1
< 0.1%

delay_weather
Real number (ℝ)

SKEWED  ZEROS 

Distinct1073
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7428505
Minimum0
Maximum1860
Zeros6671405
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size51.4 MiB
2024-06-17T14:54:01.066937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1860
Range1860
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.353928
Coefficient of variation (CV)19.322768
Kurtosis2949.5067
Mean0.7428505
Median Absolute Deviation (MAD)0
Skewness45.858291
Sum5009341
Variance206.03525
MonotonicityNot monotonic
2024-06-17T14:54:01.155223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6671405
98.9%
15 1453
 
< 0.1%
16 1340
 
< 0.1%
6 1325
 
< 0.1%
17 1298
 
< 0.1%
7 1269
 
< 0.1%
18 1258
 
< 0.1%
10 1241
 
< 0.1%
19 1238
 
< 0.1%
8 1226
 
< 0.1%
Other values (1063) 60351
 
0.9%
ValueCountFrequency (%)
0 6671405
98.9%
1 1114
 
< 0.1%
2 1188
 
< 0.1%
3 1168
 
< 0.1%
4 1140
 
< 0.1%
5 1104
 
< 0.1%
6 1325
 
< 0.1%
7 1269
 
< 0.1%
8 1226
 
< 0.1%
9 1183
 
< 0.1%
ValueCountFrequency (%)
1860 1
< 0.1%
1747 1
< 0.1%
1738 1
< 0.1%
1728 1
< 0.1%
1653 1
< 0.1%
1643 1
< 0.1%
1609 1
< 0.1%
1561 1
< 0.1%
1529 1
< 0.1%
1522 1
< 0.1%

delay_nas
Real number (ℝ)

SKEWED  ZEROS 

Distinct837
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.566957
Minimum0
Maximum1708
Zeros6082619
Zeros (%)90.2%
Negative0
Negative (%)0.0%
Memory size51.4 MiB
2024-06-17T14:54:01.224155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16
Maximum1708
Range1708
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.004842
Coefficient of variation (CV)5.8453812
Kurtosis1142.3422
Mean2.566957
Median Absolute Deviation (MAD)0
Skewness22.646317
Sum17310028
Variance225.14529
MonotonicityNot monotonic
2024-06-17T14:54:01.304171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6082619
90.2%
1 36929
 
0.5%
2 26309
 
0.4%
15 25623
 
0.4%
3 24666
 
0.4%
16 23136
 
0.3%
4 23077
 
0.3%
5 21730
 
0.3%
17 21260
 
0.3%
6 20248
 
0.3%
Other values (827) 437807
 
6.5%
ValueCountFrequency (%)
0 6082619
90.2%
1 36929
 
0.5%
2 26309
 
0.4%
3 24666
 
0.4%
4 23077
 
0.3%
5 21730
 
0.3%
6 20248
 
0.3%
7 19252
 
0.3%
8 18386
 
0.3%
9 17266
 
0.3%
ValueCountFrequency (%)
1708 1
< 0.1%
1660 1
< 0.1%
1651 1
< 0.1%
1515 1
< 0.1%
1487 1
< 0.1%
1421 1
< 0.1%
1409 2
< 0.1%
1407 1
< 0.1%
1402 1
< 0.1%
1401 1
< 0.1%

delay_security
Real number (ℝ)

SKEWED  ZEROS 

Distinct201
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.030648764
Minimum0
Maximum1460
Zeros6735256
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size51.4 MiB
2024-06-17T14:54:01.375586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1460
Range1460
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6289231
Coefficient of variation (CV)53.148083
Kurtosis179570.77
Mean0.030648764
Median Absolute Deviation (MAD)0
Skewness288.46433
Sum206677
Variance2.6533904
MonotonicityNot monotonic
2024-06-17T14:54:01.450392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6735256
99.9%
15 333
 
< 0.1%
10 293
 
< 0.1%
16 291
 
< 0.1%
17 283
 
< 0.1%
8 270
 
< 0.1%
7 258
 
< 0.1%
12 257
 
< 0.1%
18 257
 
< 0.1%
9 256
 
< 0.1%
Other values (191) 5650
 
0.1%
ValueCountFrequency (%)
0 6735256
99.9%
1 184
 
< 0.1%
2 182
 
< 0.1%
3 200
 
< 0.1%
4 186
 
< 0.1%
5 243
 
< 0.1%
6 245
 
< 0.1%
7 258
 
< 0.1%
8 270
 
< 0.1%
9 256
 
< 0.1%
ValueCountFrequency (%)
1460 1
< 0.1%
1183 1
< 0.1%
885 1
< 0.1%
808 1
< 0.1%
805 1
< 0.1%
600 1
< 0.1%
581 1
< 0.1%
449 1
< 0.1%
376 1
< 0.1%
373 1
< 0.1%

delay_lastaircraft
Real number (ℝ)

ZEROS 

Distinct1349
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6811076
Minimum0
Maximum3581
Zeros6032286
Zeros (%)89.5%
Negative0
Negative (%)0.0%
Memory size51.4 MiB
2024-06-17T14:54:01.519085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile33
Maximum3581
Range3581
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30.446469
Coefficient of variation (CV)5.3592487
Kurtosis537.39882
Mean5.6811076
Median Absolute Deviation (MAD)0
Skewness16.353585
Sum38310004
Variance926.98747
MonotonicityNot monotonic
2024-06-17T14:54:01.592915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6032286
89.5%
15 17684
 
0.3%
16 16691
 
0.2%
17 15950
 
0.2%
18 15240
 
0.2%
19 14258
 
0.2%
20 13982
 
0.2%
21 13288
 
0.2%
14 12685
 
0.2%
22 12380
 
0.2%
Other values (1339) 578960
 
8.6%
ValueCountFrequency (%)
0 6032286
89.5%
1 9092
 
0.1%
2 9550
 
0.1%
3 9331
 
0.1%
4 9511
 
0.1%
5 9753
 
0.1%
6 10693
 
0.2%
7 10528
 
0.2%
8 10884
 
0.2%
9 10977
 
0.2%
ValueCountFrequency (%)
3581 1
< 0.1%
3228 1
< 0.1%
2586 1
< 0.1%
2557 1
< 0.1%
2530 1
< 0.1%
2366 1
< 0.1%
2329 1
< 0.1%
2325 1
< 0.1%
2277 1
< 0.1%
2258 1
< 0.1%

manufacturer
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size415.9 MiB
BOEING
3122320 
AIRBUS
1981786 
EMBRAER
949752 
CANADAIR REGIONAL JET
689543 
DIAMOND AIRCRAFT
 
3

Length

Max length21
Median length6
Mean length7.6746627
Min length6

Characters and Unicode

Total characters51753351
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCANADAIR REGIONAL JET
2nd rowCANADAIR REGIONAL JET
3rd rowCANADAIR REGIONAL JET
4th rowCANADAIR REGIONAL JET
5th rowCANADAIR REGIONAL JET

Common Values

ValueCountFrequency (%)
BOEING 3122320
46.3%
AIRBUS 1981786
29.4%
EMBRAER 949752
 
14.1%
CANADAIR REGIONAL JET 689543
 
10.2%
DIAMOND AIRCRAFT 3
 
< 0.1%

Length

2024-06-17T14:54:01.670862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T14:54:01.736077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
boeing 3122320
38.4%
airbus 1981786
24.4%
embraer 949752
 
11.7%
canadair 689543
 
8.5%
regional 689543
 
8.5%
jet 689543
 
8.5%
diamond 3
 
< 0.1%
aircraft 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I 6483198
12.5%
E 6400910
12.4%
B 6053858
11.7%
A 5689719
11.0%
R 5260382
10.2%
N 4501409
8.7%
O 3811866
7.4%
G 3811863
7.4%
S 1981786
 
3.8%
U 1981786
 
3.8%
Other values (8) 5776574
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51753351
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 6483198
12.5%
E 6400910
12.4%
B 6053858
11.7%
A 5689719
11.0%
R 5260382
10.2%
N 4501409
8.7%
O 3811866
7.4%
G 3811863
7.4%
S 1981786
 
3.8%
U 1981786
 
3.8%
Other values (8) 5776574
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51753351
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 6483198
12.5%
E 6400910
12.4%
B 6053858
11.7%
A 5689719
11.0%
R 5260382
10.2%
N 4501409
8.7%
O 3811866
7.4%
G 3811863
7.4%
S 1981786
 
3.8%
U 1981786
 
3.8%
Other values (8) 5776574
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51753351
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 6483198
12.5%
E 6400910
12.4%
B 6053858
11.7%
A 5689719
11.0%
R 5260382
10.2%
N 4501409
8.7%
O 3811866
7.4%
G 3811863
7.4%
S 1981786
 
3.8%
U 1981786
 
3.8%
Other values (8) 5776574
11.2%

model
Categorical

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size399.2 MiB
737 NG
2703494 
170/175
863338 
A320
769279 
A321
704641 
CRJ
689543 
Other values (16)
1013109 

Length

Max length10
Median length7
Mean length5.0740774
Min length3

Characters and Unicode

Total characters34216554
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCRJ
2nd rowCRJ
3rd rowCRJ
4th rowCRJ
5th rowCRJ

Common Values

ValueCountFrequency (%)
737 NG 2703494
40.1%
170/175 863338
 
12.8%
A320 769279
 
11.4%
A321 704641
 
10.4%
CRJ 689543
 
10.2%
A319 390607
 
5.8%
717 184919
 
2.7%
757 156147
 
2.3%
A220 98132
 
1.5%
190/195 75262
 
1.1%
Other values (11) 108042
 
1.6%

Length

2024-06-17T14:54:01.807143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
737 2720620
28.8%
ng 2703494
28.6%
170/175 863338
 
9.1%
a320 769310
 
8.1%
a321 704641
 
7.4%
crj 689543
 
7.3%
a319 390607
 
4.1%
717 184919
 
2.0%
757 156147
 
1.7%
a220 98132
 
1.0%
Other values (10) 179723
 
1.9%

Most occurring characters

ValueCountFrequency (%)
7 7999171
23.4%
3 4630542
13.5%
1 3179671
 
9.3%
2717101
 
7.9%
N 2717070
 
7.9%
G 2717070
 
7.9%
A 1995365
 
5.8%
0 1827501
 
5.3%
2 1670215
 
4.9%
5 1118671
 
3.3%
Other values (11) 3644177
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34216554
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 7999171
23.4%
3 4630542
13.5%
1 3179671
 
9.3%
2717101
 
7.9%
N 2717070
 
7.9%
G 2717070
 
7.9%
A 1995365
 
5.8%
0 1827501
 
5.3%
2 1670215
 
4.9%
5 1118671
 
3.3%
Other values (11) 3644177
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34216554
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 7999171
23.4%
3 4630542
13.5%
1 3179671
 
9.3%
2717101
 
7.9%
N 2717070
 
7.9%
G 2717070
 
7.9%
A 1995365
 
5.8%
0 1827501
 
5.3%
2 1670215
 
4.9%
5 1118671
 
3.3%
Other values (11) 3644177
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34216554
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 7999171
23.4%
3 4630542
13.5%
1 3179671
 
9.3%
2717101
 
7.9%
N 2717070
 
7.9%
G 2717070
 
7.9%
A 1995365
 
5.8%
0 1827501
 
5.3%
2 1670215
 
4.9%
5 1118671
 
3.3%
Other values (11) 3644177
10.7%

aicraft_age
Real number (ℝ)

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.480626
Minimum1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 MiB
2024-06-17T14:54:01.876872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median12
Q320
95-th percentile25
Maximum57
Range56
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.8914947
Coefficient of variation (CV)0.58539525
Kurtosis-0.73164252
Mean13.480626
Median Absolute Deviation (MAD)6
Skewness0.29683421
Sum90905310
Variance62.275688
MonotonicityNot monotonic
2024-06-17T14:54:01.941777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
8 403056
 
6.0%
5 399399
 
5.9%
23 379337
 
5.6%
10 373106
 
5.5%
7 355234
 
5.3%
9 344489
 
5.1%
6 330746
 
4.9%
2 327333
 
4.9%
24 313636
 
4.7%
18 257439
 
3.8%
Other values (29) 3259629
48.3%
ValueCountFrequency (%)
1 186073
2.8%
2 327333
4.9%
3 169787
2.5%
4 141338
 
2.1%
5 399399
5.9%
6 330746
4.9%
7 355234
5.3%
8 403056
6.0%
9 344489
5.1%
10 373106
5.5%
ValueCountFrequency (%)
57 826
 
< 0.1%
56 1062
 
< 0.1%
48 2360
 
< 0.1%
39 645
 
< 0.1%
38 398
 
< 0.1%
34 9283
 
0.1%
33 18807
0.3%
32 34391
0.5%
31 18053
0.3%
30 27339
0.4%

Interactions

2024-06-17T14:53:37.183144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:06.565181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:10.104242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:13.520373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:16.981575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:20.491988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:23.911952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:27.189547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:30.488022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:33.861306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:37.544783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:06.901898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:10.449171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:13.880327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:17.335313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:20.830959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:24.243680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:27.518201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:30.822397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:34.196504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:37.904332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:07.235703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:10.795175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:14.229049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:17.684052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:21.171566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:24.565919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:27.849777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:31.147664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:34.520561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:38.257674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:07.566268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:11.129582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:14.584327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:18.033358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:21.584037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:24.893413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:28.173117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:31.468801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:34.846994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:38.623908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:07.909642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:11.483242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:14.921930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:18.386848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:21.910156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:25.223620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:28.507605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:31.811950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:35.180022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:38.979888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:08.260316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:11.824265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:15.268589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:18.743269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:22.246284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:25.551449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:28.848794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:32.146162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:35.511242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:39.341193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:08.597823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:12.172570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:15.606118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:19.094455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:22.579673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:25.878624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:29.174675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:32.552835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:35.841561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:39.693751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:09.080745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:12.506488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:15.943517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:19.451412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:22.908293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:26.211235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:29.506613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:32.874277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:36.173627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:40.046372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:09.416225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:12.844007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:16.278472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:19.806723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:23.245694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:26.541133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:29.838714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:33.205753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:36.498304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:40.401652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:09.754120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:13.181280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:16.628767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:20.167158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:23.580204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:26.865026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:30.167138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:33.531530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:53:36.823910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-06-17T14:53:41.939796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-17T14:53:46.538726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

flightdateday_of_weekairlinetail_numberdep_airportdep_citynamedeptime_labeldep_delaydep_delay_tagdep_delay_typearr_airportarr_citynamearr_delayarr_delay_typeflight_durationdistance_typedelay_carrierdelay_weatherdelay_nasdelay_securitydelay_lastaircraftmanufacturermodelaicraft_age
02023-01-021Endeavor AirN605LRBDLHartford, CTMorning-30Low <5minLGANew York, NY-12Low <5min56Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ16
12023-01-032Endeavor AirN605LRBDLHartford, CTMorning-50Low <5minLGANew York, NY-8Low <5min62Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ16
22023-01-043Endeavor AirN331PQBDLHartford, CTMorning-50Low <5minLGANew York, NY-21Low <5min49Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ10
32023-01-054Endeavor AirN906XJBDLHartford, CTMorning-60Low <5minLGANew York, NY-17Low <5min54Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ17
42023-01-065Endeavor AirN337PQBDLHartford, CTMorning-10Low <5minLGANew York, NY-16Low <5min50Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ10
52023-01-076Endeavor AirN336PQBDLHartford, CTMorning-100Low <5minLGANew York, NY-13Low <5min62Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ10
62023-01-146Endeavor AirN311PQLGANew York, NYAfternoon-80Low <5minCVGCincinnati, OH-31Low <5min117Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ10
72023-01-216Endeavor AirN917XJLGANew York, NYAfternoon-100Low <5minCVGCincinnati, OH-25Low <5min125Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ16
82023-01-286Endeavor AirN336PQLGANew York, NYAfternoon-50Low <5minCVGCincinnati, OH-15Low <5min130Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ10
92023-01-091Endeavor AirN491PXLGANew York, NYEvening-70Low <5minBGMBinghamton, NY-3Low <5min63Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ4
flightdateday_of_weekairlinetail_numberdep_airportdep_citynamedeptime_labeldep_delaydep_delay_tagdep_delay_typearr_airportarr_citynamearr_delayarr_delay_typeflight_durationdistance_typedelay_carrierdelay_weatherdelay_nasdelay_securitydelay_lastaircraftmanufacturermodelaicraft_age
67433942023-12-317JetBlue AirwaysN937JBBOSBoston, MAAfternoon601Medium >15minBUFBuffalo, NY43Medium >15min80Short Haul >1500Mi430000AIRBUSA32110
67433952023-12-317JetBlue AirwaysN945JTSFOSan Francisco, CAMorning-80Low <5minJFKNew York, NY-14Low <5min326Medium Haul <3000Mi00000AIRBUSA32110
67433962023-12-317JetBlue AirwaysN558JBORHWorcester, MAAfternoon-40Low <5minFLLFort Lauderdale, FL-35Low <5min169Short Haul >1500Mi00000AIRBUSA32024
67433972023-12-317JetBlue AirwaysN284JBBOSBoston, MAAfternoon-50Low <5minBWIBaltimore, MD-20Low <5min85Short Haul >1500Mi00000EMBRAER190/19516
67433982023-12-317JetBlue AirwaysN661JBJFKNew York, NYMorning201Medium >15minRSWFort Myers, FL-1Low <5min175Short Haul >1500Mi00000AIRBUSA32017
67433992023-12-317JetBlue AirwaysN903JBSJUSan Juan, PRMorning41Low <5minJFKNew York, NY-33Low <5min219Medium Haul <3000Mi00000AIRBUSA32111
67434002023-12-317JetBlue AirwaysN535JBMCOOrlando, FLEvening1131Hight >60minSJUSan Juan, PR100Hight >60min162Short Haul >1500Mi400096AIRBUSA32022
67434012023-12-317JetBlue AirwaysN354JBPHLPhiladelphia, PAAfternoon-110Low <5minBOSBoston, MA-12Low <5min73Short Haul >1500Mi00000EMBRAER190/19511
67434022023-12-317JetBlue AirwaysN768JBPBIWest Palm Beach/Palm Beach, FLAfternoon-70Low <5minBDLHartford, CT-30Low <5min158Short Haul >1500Mi00000AIRBUSA32015
67434032023-12-317JetBlue AirwaysN547JBBDLHartford, CTMorning-80Low <5minPBIWest Palm Beach/Palm Beach, FL-24Low <5min173Short Haul >1500Mi00000AIRBUSA32022

Duplicate rows

Most frequently occurring

flightdateday_of_weekairlinetail_numberdep_airportdep_citynamedeptime_labeldep_delaydep_delay_tagdep_delay_typearr_airportarr_citynamearr_delayarr_delay_typeflight_durationdistance_typedelay_carrierdelay_weatherdelay_nasdelay_securitydelay_lastaircraftmanufacturermodelaicraft_age# duplicates
02023-01-113Southwest Airlines Co.N8790QHNLHonolulu, HIMorning00Low <5minITOHilo, HI-6Low <5min54Short Haul >1500Mi00000BOEING737 NG22
12023-01-124Southwest Airlines Co.N8715QHNLHonolulu, HIMorning-20Low <5minLIHLihue, HI-5Low <5min42Short Haul >1500Mi00000BOEING737 NG72
22023-01-231Southwest Airlines Co.N8715QHNLHonolulu, HIMorning-30Low <5minITOHilo, HI-8Low <5min55Short Haul >1500Mi00000BOEING737 NG72
32023-02-271Southwest Airlines Co.N235WNAUSAustin, TXAfternoon-70Low <5minDALDallas, TX-22Low <5min50Short Haul >1500Mi00000BOEING737 NG182
42023-03-035Southwest Airlines Co.N8787KHNLHonolulu, HIMorning-10Low <5minOGGKahului, HI-5Low <5min41Short Haul >1500Mi00000BOEING737 NG22
52023-03-142Southwest Airlines Co.N8546VRICRichmond, VAAfternoon-70Low <5minATLAtlanta, GA-15Low <5min97Short Haul >1500Mi00000BOEING737 NG72
62023-03-234Skywest Airlines Inc.N823SKSGUSt. George, UTMorning-60Low <5minSLCSalt Lake City, UT-18Low <5min63Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ162
72023-04-112Skywest Airlines Inc.N823SKSLCSalt Lake City, UTAfternoon-50Low <5minSGUSt. George, UT-13Low <5min60Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ162
82023-04-193Skywest Airlines Inc.N823SKSLCSalt Lake City, UTAfternoon-30Low <5minSGUSt. George, UT-13Low <5min58Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ162
92023-05-066Republic AirwaysN824MDCMHColumbus, OHMorning-40Low <5minDTWDetroit, MI-25Low <5min49Short Haul >1500Mi00000EMBRAER170/175192